Datafold

Your best data engineer didn’t sign up to be a human diffing tool. But during a migration, that’s what they become: buried in SQL rewrites and manual validation for months. It’s one of the lowest-leverage ways to spend your highest-leverage talent. What could they be building instead? Our latest from Leo and Elliot: https://lnkd.in/gwTcDn5V

2025 is shaping up to be a turning point for data migrations. We broke down 4 trends every data team should have on their radar. https://lnkd.in/g87QsxWG

6x faster MySQL-to-Snowflake migration? Yes, it’s possible. A leading healthcare staffing firm cut their migration timeline from 6 months to 1 month—while reducing costs by 80%—using Datafold’s AI-powered Migration Agent. The challenge: ? 1,000+ MySQL queries with complex joins and transformations. ? Zero room for discrepancies in credentialing reports used to verify healthcare professionals. ? Manual validation? Not scalable or risk-free. With Datafold: ? AI-powered SQL translation refined through continuous validation. ? Cross-database diffing for perfect parity. ? Pull request-based delivery for seamless CI/CD integration. The result? A fully automated, validated migration without contractor overhead or manual query rewrites. If you’re facing a migration, why settle for months of manual work? Let automation do the heavy lifting. Curious how it works? Learn more here: https://lnkd.in/gDg9gNR6

Everyone knows migrations are hard. But no one realizes how hard their migration will be, until they’re in the middle of it. That was the recurring theme in every conversation we had at Gartner. Teams are drowning in edge cases: ?? 15 years of ETL pipelines with undocumented business logic. ?? Stored procedures that have evolved into an entire application. ?? A pile of brittle scripts: some critical, some forgotten. The real challenge isn’t the big, obvious problems. It’s the long tail of weird exceptions that no tool fully anticipates. That’s why so many companies stay frozen in place, still running on legacy stacks long after signing their cloud contracts. Naturally, plenty of AI skepticism came up. “Can AI really handle something this complex?” We get it. Most “AI-driven” tools are just glorified rule-based automation. But Datafold's Migration Agent doesn’t just migrate code. It understands context, dependencies, and data. Graham Whitelaw, CFA breaks it all down in his latest post: https://lnkd.in/gYydqxVs

Data migrations should be simple: move records from one system to another while keeping everything intact. But anyone who's been through one knows that’s rarely how it plays out: ? Schema drift breaks queries. ? Type mismatches corrupt financial figures. ? Duplicate records quietly distort analytics. Even a single mismapped field can trigger cascading data integrity issues. Just ask TSB Bank, whose failed migration in 2018 locked out millions of customers. Most migration failures don’t happen all at once. They build up over time through manual code translation, incomplete validation, and hidden errors that only surface after go-live. The fix? Automated, row-level validation before cutover. Instead of hoping everything aligns, Datafold’s Migration Agent ensures 100% parity across datasets so teams can migrate with confidence, not guesswork. How does your team catch migration errors before they hit production? https://lnkd.in/gW-kpACG

Data warehouse migrations should make life easier with faster queries, lower costs, and better scalability. But what if your reports break the day after go-live? Migrations often introduce hidden errors: ? Schema drift changes data types unexpectedly. ? Records go missing or duplicate without warning. ? Corrupt data quietly distorts business insights. Fixing these problems after migration is a nightmare. That’s why automated validation is essential: it catches issues before they impact operations. Want to future-proof your migration? Check out our latest article: https://lnkd.in/gtSS8cUg

AI in data engineering is still evolving, but it’s already reshaping how teams build and manage data. ? Need SQL? AI can generate and optimize queries. ? Code reviews slowing you down? AI can surface key issues in seconds. ? Migrating data? AI can automate cross-database validation. The benefits are clear: more efficiency, fewer manual tasks, and lower costs. But challenges remain: data security, AI readiness, and knowing when AI shouldn’t be used. We break it all down in our latest article by Kira. Read it here: https://lnkd.in/gGa5hPVQ

?? We just read this insightful analysis from New Enterprise Associates (NEA) on "How AI Will Modernize Enterprise Code," and it resonates deeply with what we're seeing in the data space. The core insight? "Behind every innovator's dilemma is a legacy code base." Replace "code base" with "data infrastructure," and you've captured the exact challenge we heard from data leaders at #GartnerDA this week. Three key parallels that stand out: 1?? Legacy systems create technical debt that blocks innovation: Whether it's outdated code or archaic data warehouses, maintaining the old consumes resources needed for building the new. 2?? Migration requires more than AI code generation: NEA highlights how winning solutions combine "generative AI, traditional static analysis, and human review." Similarly, successful data migrations need more than just SQL translation—they require validation, testing, and context. 3?? The vision of "self-healing" systems: NEA envisions "proactive, automated testing," leading to "continuous, automated code modernization and less technical debt." This perfectly aligns with the direction of modern data infrastructure: automation that continuously validates and optimizes data pipelines. The paper from Madison Faulkner and Maanasi Garg features Datafold as an example of building a platform that enables "recurring migration and modernization capabilities" through validation flywheels—exactly what we're passionate about. This framework offers a valuable perspective on the path forward for data leaders who feel stuck between legacy constraints and AI aspirations. Read the full analysis here:?https://lnkd.in/gbd_CsG5

If you're at #GartnerDA this week, make sure to stop by the Datafold booth to talk all things data migrations, data quality, and automating data engineering workflows ?? And don't miss Gleb Mezhanskiy's talk "From Years to Weeks: How AI is Speeding Up Data Warehouse Migrations" this Wednesday at 1:05 pm EST.

?? Exciting news! Datafold is sponsoring and attending the Gartner Data & Analytics Summit from March 3-5 in Orlando, FL. We’re looking forward to insightful discussions on AI, data quality, and accelerating data workflows—and, of course, meeting data practitioners who are driving the future of analytics! If you're attending, let’s connect! Drop by our booth or DM us to schedule a chat. See you there ?? #GartnerDA